Trends in the development of artificial intelligence technologies: the economic and legal aspect fo. 2)
Bibliographic record
Abstract
The economic-legal analysis of the state and trends of the development of technologies of artificial intelligence (AI) has been carried out. The influence of AI on the development of society, economic effect, methods and the field of application, the state of developments in the world and Ukraine are analyzed. In the next decade, AI will become the main market trend and the best business tool. The contribution of intellectual technologies to global GDP is estimated at 15.7 trillion. dollars In the next 5-10 years, China will be the leader in the successful operation and adaptation of AI technologies. According to analysts, the most benefit from AI technologies will be in the areas of financial services, retail and medicine.The scientific and inventive activity in the sphere of AI, the role of protection of intellectual property (patent and copyright), and the maintenance of the balance of competing interests are researched. Recently, the number of inventions based on AI has sharply increased. The leaders in the number of such inventions are American companies IBM and Microsoft. This growth is due to the fact that in recent years AI has evolved from the theoretical concept into a real product that gains the world market. Since the advent of AI in the 50’s of the last century, inventors and researchers have applied for almost 340 thousand inventions based on AI (as of the end of 2016) and published more than 1.6 million scientific articles. The transport sector, including autonomous vehicles, is one of the sectors with the highest rates of growth in the application of AI. China has become a global leader in increasing the number of patents in the AI sphere over the past five years.By the number of companies working in the sphere of AI, Ukraine is among the three leaders among the countries of Eastern Europe. There are 57 AI companies in Ukraine and it has 11 investorsGeneralized practice of state regulation of activity in the sphere of AI in industrialized countries and EU countries. More and more countries are developing national AI strategies. Thus, 17 countries, including Canada, China, Denmark, France, India, South Korea and Taiwan, have already announced their AI strategies. Some of them invest billions of dollars in this area. China, for example, has invested more than $ 10 billion in this technological trend, followed by South Korea — $ 2 billion and France — $ 1.5 billion. Governmental structures from different countries are concerned about the need to develop relevant national strategies, programs and regulation of AI legislative level. Identified existing problems and suggested ways to solve them. Problems constraining the development of AI in Ukraine: the absence of a strategy for the development of AI, the domestic infrastructure for its work and the weakness of the business about existing fundamental scientific developments in the field of AI, insufficient for the implementation of AI level of digitalization of companies, the lack of a high level of data work, and is also a misunderstanding of the implementation guidance in the AI company.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".